prioritizr (version 3.0.4)

add_loglinear_targets: Add Loglinear Targets

Description

Set targets as a proportion (between 0 and 1) and calculated using a log-linear equation and four tuning parameters (as used in Rodrigues et al. 2004). The first tuning parameter specifies the first cut-off range size, and the second specifies the second cut-off range size, the third argument specifies the target required for species with a range size equal to or less than the first cut-off range size, and the fourth argument specifies the target required for species with a range size equal to or greater than the required range size.

Usage

add_loglinear_targets(x, lower_bound_amount, lower_bound_target,
  upper_bound_amount, upper_bound_target)

Arguments

x

ConservationProblem-class object.

lower_bound_amount

numeric lower bound for the total amount of the features.

lower_bound_target

numeric relative target that should be applied to features with a total amount that is less than or equal to lower_bound_amount.

upper_bound_amount

numeric upper bound for the total amount of features.

upper_bound_target

numeric relative target that should be applied to features with a total amount that is greater than or equal to upper_bound_amount.

...

not used.

Value

ConservationProblem-class object with the target added to it.

Details

Note that with the exception of the maximum cover problem, targets must be added to a problem or solving will return an error.

Targets are used to specify the minimum amount or proportion of a feature's distribution that needs to be protected. All conservation planning problems require adding targets with the exception of the maximum cover problem (see add_max_cover_objective), which maximizes all features in the solution and therefore does not require targets.

References

Rodrigues ASL, Akcakaya HR, Andelman SJ, Bakarr MI, Boitani L, Brooks TM, Chanson JS, Fishpool LDC, da Fonseca GAB, Gaston KJ, and others (2004) Global gap analysis: priority regions for expanding the global protected-area network. BioScience, 54: 1092--1100.

See Also

targets.

Examples

# NOT RUN {
# load data
data(sim_pu_raster, sim_features)

# create problem using loglinear targets
p <- problem(sim_pu_raster, sim_features) %>%
     add_min_set_objective() %>%
     add_loglinear_targets(10, 0.9, 100, 0.2) %>%
     add_binary_decisions()
# }
# NOT RUN {
# solve problem
s <- solve(p)

# plot solution
plot(s, main = "solution", axes = FALSE, box = FALSE)
# }
# NOT RUN {
# }